Object location using the hough transform

Abstract

This chapter considers how best to locate 2D object shapes in digital images. Simple paradigms such as boundary tracking followed by analysis of centroidal shape profiles lack robustness and are incapable of coping with occlusion, breakage or gross shape distortions, whereas the Hough transform readily overcomes these problems as an integral property of its voting schema. This is an important advantage, especially in practical situations such as those where lighting is non-ideal. In addition, the Hough transform is excellent at locating straight lines, circles, ellipses and other well defined shapes: it can even locate arbitrary shapes that are defined by so-called R-tables. The method also has the advantage of being able to make sense of patterns of point features, and is far less computation intensive than the maximal clique graph matching method often used for this purpose. Being a form of spatial matched filter, the Hough transform has high signal-to-noise sensitivity, and for simple shapes is highly efficient, but when applied to arbitrary shapes of variable orientation, size and individual variability, measures have to be taken to limit its computation. Overall, the Hough transform provides a very useful, robust search technique for locating known types of object in digital images.